“Scribe is a server for aggregating log data streamed in real time from a large number of servers. It is designed to be scalable, extensible without client-side modification, and robust to failure of the network or any specific machine.”

Scribe servers are arranged in a directed graph, with each server knowing only about the next server in the graph. This network topology allows for adding extra layers of fan-in as a system grows, and batching messages before sending them between datacenters, without having any code that explicitly needs to understand datacenter topology, only a simple configuration.
Scribe was designed to consider reliability but to not require heavyweight protocols and expansive disk usage.

“McDipper is a highly performant flash-based cache server that is Memcache protocol compatible.”

Facebook heavily use Memcached in early stages and when RAM’s cost became too expensive had to find a different solution. This is why McDipper was born. It is not different then other projects like Fatcache or Edis. As many DBMS did, they try to mimic an in-memory caching engine in order to extend available space. SSD are fast enough to have no problem about speed loss.

“The new photo infrastructure merges the photo serving tier and storage tier into one physical tier. It implements a HTTP based photo server which stores photos in a generic object store called Haystack.”

A system specifically designed to serve static content using HTTP as fast as possible. Haystack can be broken down into these functional layers –

“Here at Facebook, we’re likely the world’s largest user of memcached. We use memcached to alleviate database load. memcached is already fast, but we need it to be faster and more efficient than most installations.”

“Storm is a distributed realtime computation system. Similar to how Hadoop provides a set of general primitives for doing batch processing, Storm provides a set of general primitives for doing realtime computation.”

There are just three abstractions in Storm: spouts, bolts, and topologies.

A spout is a source of streams in a computation. Typically a spout reads from a queueing broker such as Kestrel, RabbitMQ, or Kafka, but a spout can also generate its own stream or read from somewhere.

A bolt processes any number of input streams and produces any number of new output streams. Most of the logic of a computation goes into bolts, such as functions, filters, streaming joins, streaming aggregations, talking to databases, and so on.

A topology is a network of spouts and bolts, with each edge in the network representing a bolt subscribing to the output stream of some other spout or bolt.

“Snowflake is a network service for generating unique ID numbers at high scale with some simple guarantees.”

Twitter needed something that could generate tens of thousands of ids per second in a highly available manner. This naturally led us to choose an uncoordinated approach.

These ids need to be roughly sortable, meaning that if tweets A and B are posted around the same time, they should have ids in close proximity to one another since this is how we and most Twitter clients sort tweets. Additionally, these numbers have to fit into 64 bits.

To generate them in an uncoordinated manner, we settled on a composition of: timestamp, worker number and sequence number. Sequence numbers are per-thread and worker numbers are chosen at startup via zookeeper (though that’s overridable via a config file).

To minimize the number of small, random writes, fatcache treats the SSD as a log-structured object store. All writes are aggregated in memory and written to the end of the circular log in batches – usually multiples of 1 MB.

“FlockDB is a database that stores graph data, but it isn’t a database optimized for graph-traversal operations. Instead, it’s optimized for very large adjacency lists, fast reads and writes, and page-able set arithmetic queries.”

It is a distributed graph database for storing adjacency lists, with goals of supporting:

FlockDB is much simpler than other graph databases such as Neo4j because it tries to solve fewer problems. It scales horizontally and is designed for on-line, low-latency, high throughput environments such as web-sites.

“Spark is an open source cluster computing system that aims to make data analytics fast both fast to run and fast to write. To run programs faster, Spark provides primitives for in-memory cluster computing: your job can load data into memory and query it repeatedly much more quickly than with disk-based systems like Hadoop MapReduce.”

Spark was initially developed for two applications where keeping data in memory helps: iterative algorithms, which are common in machine learning, and interactive data mining. In both cases, Spark can run up to100x faster than Hadoop MapReduce. However, you can use Spark for general data processing too.

“Shark is a large-scale data warehouse system for Spark designed to be compatible with Apache Hive. It can execute Hive QL queries up to 100 times faster than Hive without any modification to the existing data or queries. Shark supports Hive’s query language, metastore, serialization formats, and user-defined functions, providing seamless integration with existing Hive deployments and a familiar, more powerful option for new ones.”

Shark is built on top of Spark, a data-parallel execution engine that is fast and fault-tolerant. Even if data are on disk, Shark can be noticeably faster than Hive because of the fast execution engine. It avoids the high task launching overhead of Hadoop MapReduce and does not require materializing intermediate data between stages on disk. Thanks to this fast engine, Shark can answer queries in sub-second latency.

The fundamental problem with logs is that they are usually stored in files although they are best represented as streams (by Adam Wiggins, CTO at Heroku). Traditionally, they have been dumped into text-based files and collected by rsync in hourly or daily fashion. With today’s web/mobile applications, this creates two problems.

Need ad-hoc parsing: The text-based logs have their own format, and analytics engineer need to write a dedicated parser for each format. But that’s probably not the best use of your time. You should be analyzing data to make better business decisions instead of writing one parser after another.

Lacks Freshness: The logs lag. The realtime analysis of user behavior makes feature iterations a lot faster. A nimbler A/B testing will help you differentiate your service from competitors.

This is where Fluentd comes in. We believe Fluentd solves all issues of scalable log collection by getting rid of files and turning logs into true semi-structured data streams.

“Kestrel is a very simple message queue that runs on the JVM. It supports multiple protocols: memcache: the memcache protocol, with some extensions, thrift: Apache Thrift-based RPC, text: a simple text-based protocol”

A cluster of kestrel servers is like a memcache cluster: the servers don’t know about each other, and don’t do any cross-communication, so you can add as many as you like. The simplest clients have a list of all servers in the cluster, and pick one at random for each operation. In this way, each queue appears to be spread out across every server, with items in a loose ordering. More advanced clients can find kestrel servers via ZooKeeper.

With Impala, you can query data, whether stored in HDFS or Apache HBase – including SELECT, JOIN, and aggregate functions – in real time.

To avoid latency, Impala circumvents MapReduce to directly access the data through a specialized distributed query engine that is very similar to those found in commercial parallel RDBMSs. The result is order-of-magnitude faster performance than Hive, depending on the type of query and configuration. (See FAQ below for more details.) Note that this performance improvement has been confirmed by several large companies that have tested Impala on real-world workloads for several months now.

“Hypertable is an open source database system inspired by publications on the design of Google’s BigTable. The project is based on experience of engineers who were solving large-scale data-intensive tasks for many years.”

This project is for the design and implementation of a high performance, scalable, distributed storage and processing system for structured and unstructured data. It is designed to manage the storage and processing of information on a large cluster of commodity servers, providing resilience to machine and component failures. Data is represented in the system as a multi-dimensional table of information. The data in a table can be transformed and organized at high speed by performing computations in parallel, pushing them to where the data is physically stored.